unable to remove spatial autocorrelation from a binomial gam

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unable to remove spatial autocorrelation from a binomial gam

Carlos Bautista
Dear list members,

I am using gam (from mgcv package in R) to model presence/absence data in
3355 cells of 1x1km (151 presences and 3204 absences). Even though I
include a smooth with the spatial locations in the model to address the
spatial dependence in my data, the results from a variogram show spatial
autocorrelation in the residuals of my gam (range=6000 meters). Since I am
modelling a binary response, using a gamm with a correlation structure is
not advisable because it "performs poorly with binary data", neither gamm4
because (although is supposed to be appropriate for binary data) it has "no
facility for nlme style correlation structures".

The alternative I have found is to fit my model using the function magic
from the same mgcv package. Because I found no examples of how to use magic
for spatially correlated data I have adapted the ?magic example for
temporally correlated data. The results of the output change the
coefficients of the model but do not remove the spatial autocorrelation and
the smooth plots show the same effect.
You can find find the output from my models and figures of the variograms
and plots of the smooth effects in the following link
https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r


Could someone tell me if there is something wrong in my script? Does anyone
know another alternative to remove the residuals' spatial autocorrelation
from a binomial gam?

Thank you very much.
Kind regards,
Carlos
--
Carlos Bautista
Institute of Nature Conservation
Polish Academy of Sciences
Mickiewicza 33
31-120 Krakow, Poland
www.carpathianbear.pl
www.iop.krakow.pl

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Re: unable to remove spatial autocorrelation from a binomial gam

Olga Boet
Hi Carlos,

Excuse me, I don't sure that I can help you, I know little about GAM. I
don’t understand your script and variogram, I work different. I hope
someone else gives you a better answer than mine. But if it can help, here
are some considerations.

Spatial data is often correlated, but it must be evaluated if it is a
problem or not. For exemple, some species are distributed by stains as
frogs, fihes or some plants species (this correlation should not be
eliminated).

I think the smooothing function in GAM is to smooth the curves, that is, it
softens (less abrupt) the effect of environmental variables (not the
coordinates, since the coordinates are not environmental variables in a
spatial model).

However, in Dimo package, there are two interesting functions: balancing
weights function and thinning function.

Balance function is weightCases(), and it is used when the background is
very large with respect to the number of presences. So that the values of
the variables in the presence points have more weight in the model despite
the lower number.

Thinning function removes points that are too close to each other (or in a
space where variable data is not available). It is used when there are
points that are too clustered as a result of sampling (but it does not
correspond to the actual distribution). In this function you can determine
the minimum distance between the points.

thinning() is from package spThin (URL:
https://cran.r-project.org/web/packages/spThin)


Finally, are your data really presence/absence data? did you go to at 3355
cells and detect presence/absence of the species? spatial models are
different if we have absences, pseudoabsences or backround. The type of
absence data is important for choosing a model.


I'm sorry I couldn't answer your questions



Kind regards,


Olga Boet
Documentalista de la col·lecció de cordats. CMCNB
*Myrmex*


Missatge de Carlos Bautista <[hidden email]> del dia dj., 9
d’abr. 2020 a les 17:52:

> Dear list members,
>
> I am using gam (from mgcv package in R) to model presence/absence data in
> 3355 cells of 1x1km (151 presences and 3204 absences). Even though I
> include a smooth with the spatial locations in the model to address the
> spatial dependence in my data, the results from a variogram show spatial
> autocorrelation in the residuals of my gam (range=6000 meters). Since I am
> modelling a binary response, using a gamm with a correlation structure is
> not advisable because it "performs poorly with binary data", neither gamm4
> because (although is supposed to be appropriate for binary data) it has "no
> facility for nlme style correlation structures".
>
> The alternative I have found is to fit my model using the function magic
> from the same mgcv package. Because I found no examples of how to use magic
> for spatially correlated data I have adapted the ?magic example for
> temporally correlated data. The results of the output change the
> coefficients of the model but do not remove the spatial autocorrelation and
> the smooth plots show the same effect.
> You can find find the output from my models and figures of the variograms
> and plots of the smooth effects in the following link
>
> https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r
>
>
> Could someone tell me if there is something wrong in my script? Does anyone
> know another alternative to remove the residuals' spatial autocorrelation
> from a binomial gam?
>
> Thank you very much.
> Kind regards,
> Carlos
> --
> Carlos Bautista
> Institute of Nature Conservation
> Polish Academy of Sciences
> Mickiewicza 33
> 31-120 Krakow, Poland
> www.carpathianbear.pl
> www.iop.krakow.pl
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>

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Re: unable to remove spatial autocorrelation from a binomial gam

Carlos Bautista
Hello Olga

Thanks a lot for your response. It is very helpful.

Yes, my data is presence/absence because I'm observing the occurrence of
bear damaging apiaries in a particular region. Since there is a
compensation system that is running for a long time we can assume that
almost all damage is included in the database. So perhaps a few absences
could be presences (a beekeeper not claiming the damage) but I'm
pretty sure that it'd be marginal. I have also read what you say about
environmental data not being always an issue that should be removed from a
model. But in some books and articles, it is written that properly
accounting for autocorrelation is necessary for obtaining reliable
statistical inference (
http://highstat.com/index.php/mixed-effects-models-and-extensions-in-ecology-with-r
 see also here
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.1674 ). What
should I follow? So far my approach is more conservative and I try to
remove since I imagine reviewers asking me to do so.

I knew about the possibility of subsampling to avoid autocorrelation but
I've read that it's not the best solution. That's why I was trying to use
correlation structures. I have got the advice to use the function gamm that
allow such correlations and check if the model fit is more ore less similar
to the one of a gam model. I am in the middle of that now and waiting for
the gamm to finish as it is computationally costly (it may take a few days).
I didn't know about the package that you recommended so I will take a
look at it. Maybe the weightCases() function will be a good solution to my
problem.

Thank you so much once again for your help.

All the best,
Carlos

On Fri, 10 Apr 2020 at 12:04, Olga Boet <[hidden email]> wrote:

> Hi Carlos,
>
> Excuse me, I don't sure that I can help you, I know little about GAM. I
> don’t understand your script and variogram, I work different. I hope
> someone else gives you a better answer than mine. But if it can help, here
> are some considerations.
>
> Spatial data is often correlated, but it must be evaluated if it is a
> problem or not. For exemple, some species are distributed by stains as
> frogs, fihes or some plants species (this correlation should not be
> eliminated).
>
> I think the smooothing function in GAM is to smooth the curves, that is,
> it softens (less abrupt) the effect of environmental variables (not the
> coordinates, since the coordinates are not environmental variables in a
> spatial model).
>
> However, in Dimo package, there are two interesting functions: balancing
> weights function and thinning function.
>
> Balance function is weightCases(), and it is used when the background is
> very large with respect to the number of presences. So that the values of
> the variables in the presence points have more weight in the model despite
> the lower number.
>
> Thinning function removes points that are too close to each other (or in a
> space where variable data is not available). It is used when there are
> points that are too clustered as a result of sampling (but it does not
> correspond to the actual distribution). In this function you can determine
> the minimum distance between the points.
>
> thinning() is from package spThin (URL:
> https://cran.r-project.org/web/packages/spThin)
>
>
> Finally, are your data really presence/absence data? did you go to at 3355
> cells and detect presence/absence of the species? spatial models are
> different if we have absences, pseudoabsences or backround. The type of
> absence data is important for choosing a model.
>
>
> I'm sorry I couldn't answer your questions
>
>
>
> Kind regards,
>
>
> Olga Boet
> Documentalista de la col·lecció de cordats. CMCNB
> *Myrmex*
>
>
> Missatge de Carlos Bautista <[hidden email]> del dia dj., 9
> d’abr. 2020 a les 17:52:
>
>> Dear list members,
>>
>> I am using gam (from mgcv package in R) to model presence/absence data in
>> 3355 cells of 1x1km (151 presences and 3204 absences). Even though I
>> include a smooth with the spatial locations in the model to address the
>> spatial dependence in my data, the results from a variogram show spatial
>> autocorrelation in the residuals of my gam (range=6000 meters). Since I am
>> modelling a binary response, using a gamm with a correlation structure is
>> not advisable because it "performs poorly with binary data", neither gamm4
>> because (although is supposed to be appropriate for binary data) it has
>> "no
>> facility for nlme style correlation structures".
>>
>> The alternative I have found is to fit my model using the function magic
>> from the same mgcv package. Because I found no examples of how to use
>> magic
>> for spatially correlated data I have adapted the ?magic example for
>> temporally correlated data. The results of the output change the
>> coefficients of the model but do not remove the spatial autocorrelation
>> and
>> the smooth plots show the same effect.
>> You can find find the output from my models and figures of the variograms
>> and plots of the smooth effects in the following link
>>
>> https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r
>>
>>
>> Could someone tell me if there is something wrong in my script? Does
>> anyone
>> know another alternative to remove the residuals' spatial autocorrelation
>> from a binomial gam?
>>
>> Thank you very much.
>> Kind regards,
>> Carlos
>> --
>> Carlos Bautista
>> Institute of Nature Conservation
>> Polish Academy of Sciences
>> Mickiewicza 33
>> 31-120 Krakow, Poland
>> www.carpathianbear.pl
>> www.iop.krakow.pl
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>

--
Carlos Bautista
Institute of Nature Conservation
Polish Academy of Sciences
Mickiewicza 33
31-120 Krakow, Poland
www.carpathianbear.pl
www.iop.krakow.pl

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Re: unable to remove spatial autocorrelation from a binomial gam

Manuel Spínola
Hello Carlos,

May be you want to take a look on the GSIF and spm packages.

Manuel

El dom., 12 abr. 2020 a las 15:11, Carlos Bautista (<
[hidden email]>) escribió:

> Hello Olga
>
> Thanks a lot for your response. It is very helpful.
>
> Yes, my data is presence/absence because I'm observing the occurrence of
> bear damaging apiaries in a particular region. Since there is a
> compensation system that is running for a long time we can assume that
> almost all damage is included in the database. So perhaps a few absences
> could be presences (a beekeeper not claiming the damage) but I'm
> pretty sure that it'd be marginal. I have also read what you say about
> environmental data not being always an issue that should be removed from a
> model. But in some books and articles, it is written that properly
> accounting for autocorrelation is necessary for obtaining reliable
> statistical inference (
>
> http://highstat.com/index.php/mixed-effects-models-and-extensions-in-ecology-with-r
>  see also here
> https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.1674 ). What
> should I follow? So far my approach is more conservative and I try to
> remove since I imagine reviewers asking me to do so.
>
> I knew about the possibility of subsampling to avoid autocorrelation but
> I've read that it's not the best solution. That's why I was trying to use
> correlation structures. I have got the advice to use the function gamm that
> allow such correlations and check if the model fit is more ore less similar
> to the one of a gam model. I am in the middle of that now and waiting for
> the gamm to finish as it is computationally costly (it may take a few
> days).
> I didn't know about the package that you recommended so I will take a
> look at it. Maybe the weightCases() function will be a good solution to my
> problem.
>
> Thank you so much once again for your help.
>
> All the best,
> Carlos
>
> On Fri, 10 Apr 2020 at 12:04, Olga Boet <[hidden email]> wrote:
>
> > Hi Carlos,
> >
> > Excuse me, I don't sure that I can help you, I know little about GAM. I
> > don’t understand your script and variogram, I work different. I hope
> > someone else gives you a better answer than mine. But if it can help,
> here
> > are some considerations.
> >
> > Spatial data is often correlated, but it must be evaluated if it is a
> > problem or not. For exemple, some species are distributed by stains as
> > frogs, fihes or some plants species (this correlation should not be
> > eliminated).
> >
> > I think the smooothing function in GAM is to smooth the curves, that is,
> > it softens (less abrupt) the effect of environmental variables (not the
> > coordinates, since the coordinates are not environmental variables in a
> > spatial model).
> >
> > However, in Dimo package, there are two interesting functions: balancing
> > weights function and thinning function.
> >
> > Balance function is weightCases(), and it is used when the background is
> > very large with respect to the number of presences. So that the values of
> > the variables in the presence points have more weight in the model
> despite
> > the lower number.
> >
> > Thinning function removes points that are too close to each other (or in
> a
> > space where variable data is not available). It is used when there are
> > points that are too clustered as a result of sampling (but it does not
> > correspond to the actual distribution). In this function you can
> determine
> > the minimum distance between the points.
> >
> > thinning() is from package spThin (URL:
> > https://cran.r-project.org/web/packages/spThin)
> >
> >
> > Finally, are your data really presence/absence data? did you go to at
> 3355
> > cells and detect presence/absence of the species? spatial models are
> > different if we have absences, pseudoabsences or backround. The type of
> > absence data is important for choosing a model.
> >
> >
> > I'm sorry I couldn't answer your questions
> >
> >
> >
> > Kind regards,
> >
> >
> > Olga Boet
> > Documentalista de la col·lecció de cordats. CMCNB
> > *Myrmex*
> >
> >
> > Missatge de Carlos Bautista <[hidden email]> del dia dj.,
> 9
> > d’abr. 2020 a les 17:52:
> >
> >> Dear list members,
> >>
> >> I am using gam (from mgcv package in R) to model presence/absence data
> in
> >> 3355 cells of 1x1km (151 presences and 3204 absences). Even though I
> >> include a smooth with the spatial locations in the model to address the
> >> spatial dependence in my data, the results from a variogram show spatial
> >> autocorrelation in the residuals of my gam (range=6000 meters). Since I
> am
> >> modelling a binary response, using a gamm with a correlation structure
> is
> >> not advisable because it "performs poorly with binary data", neither
> gamm4
> >> because (although is supposed to be appropriate for binary data) it has
> >> "no
> >> facility for nlme style correlation structures".
> >>
> >> The alternative I have found is to fit my model using the function magic
> >> from the same mgcv package. Because I found no examples of how to use
> >> magic
> >> for spatially correlated data I have adapted the ?magic example for
> >> temporally correlated data. The results of the output change the
> >> coefficients of the model but do not remove the spatial autocorrelation
> >> and
> >> the smooth plots show the same effect.
> >> You can find find the output from my models and figures of the
> variograms
> >> and plots of the smooth effects in the following link
> >>
> >>
> https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r
> >>
> >>
> >> Could someone tell me if there is something wrong in my script? Does
> >> anyone
> >> know another alternative to remove the residuals' spatial
> autocorrelation
> >> from a binomial gam?
> >>
> >> Thank you very much.
> >> Kind regards,
> >> Carlos
> >> --
> >> Carlos Bautista
> >> Institute of Nature Conservation
> >> Polish Academy of Sciences
> >> Mickiewicza 33
> >> 31-120 Krakow, Poland
> >> www.carpathianbear.pl
> >> www.iop.krakow.pl
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> R-sig-Geo mailing list
> >> [hidden email]
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>
> >
>
> --
> Carlos Bautista
> Institute of Nature Conservation
> Polish Academy of Sciences
> Mickiewicza 33
> 31-120 Krakow, Poland
> www.carpathianbear.pl
> www.iop.krakow.pl
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>


--
*Manuel Spínola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
[hidden email] <[hidden email]>
[hidden email]
Teléfono: (506) 8706 - 4662
Personal website: Lobito de río <https://sites.google.com/site/lobitoderio/>
Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>

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Re: unable to remove spatial autocorrelation from a binomial gam

Carlos Bautista
Hello Manuel.

Thanks a lot. I'll take a look at them.

All the best
Carlos

On Mon, 13 Apr 2020 at 00:07, Manuel Spínola <[hidden email]> wrote:

> Hello Carlos,
>
> May be you want to take a look on the GSIF and spm packages.
>
> Manuel
>
> El dom., 12 abr. 2020 a las 15:11, Carlos Bautista (<
> [hidden email]>) escribió:
>
>> Hello Olga
>>
>> Thanks a lot for your response. It is very helpful.
>>
>> Yes, my data is presence/absence because I'm observing the occurrence of
>> bear damaging apiaries in a particular region. Since there is a
>> compensation system that is running for a long time we can assume that
>> almost all damage is included in the database. So perhaps a few absences
>> could be presences (a beekeeper not claiming the damage) but I'm
>> pretty sure that it'd be marginal. I have also read what you say about
>> environmental data not being always an issue that should be removed from a
>> model. But in some books and articles, it is written that properly
>> accounting for autocorrelation is necessary for obtaining reliable
>> statistical inference (
>>
>> http://highstat.com/index.php/mixed-effects-models-and-extensions-in-ecology-with-r
>>  see also here
>> https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.1674 ). What
>> should I follow? So far my approach is more conservative and I try to
>> remove since I imagine reviewers asking me to do so.
>>
>> I knew about the possibility of subsampling to avoid autocorrelation but
>> I've read that it's not the best solution. That's why I was trying to use
>> correlation structures. I have got the advice to use the function gamm
>> that
>> allow such correlations and check if the model fit is more ore less
>> similar
>> to the one of a gam model. I am in the middle of that now and waiting for
>> the gamm to finish as it is computationally costly (it may take a few
>> days).
>> I didn't know about the package that you recommended so I will take a
>> look at it. Maybe the weightCases() function will be a good solution to my
>> problem.
>>
>> Thank you so much once again for your help.
>>
>> All the best,
>> Carlos
>>
>> On Fri, 10 Apr 2020 at 12:04, Olga Boet <[hidden email]> wrote:
>>
>> > Hi Carlos,
>> >
>> > Excuse me, I don't sure that I can help you, I know little about GAM. I
>> > don’t understand your script and variogram, I work different. I hope
>> > someone else gives you a better answer than mine. But if it can help,
>> here
>> > are some considerations.
>> >
>> > Spatial data is often correlated, but it must be evaluated if it is a
>> > problem or not. For exemple, some species are distributed by stains as
>> > frogs, fihes or some plants species (this correlation should not be
>> > eliminated).
>> >
>> > I think the smooothing function in GAM is to smooth the curves, that is,
>> > it softens (less abrupt) the effect of environmental variables (not the
>> > coordinates, since the coordinates are not environmental variables in a
>> > spatial model).
>> >
>> > However, in Dimo package, there are two interesting functions: balancing
>> > weights function and thinning function.
>> >
>> > Balance function is weightCases(), and it is used when the background is
>> > very large with respect to the number of presences. So that the values
>> of
>> > the variables in the presence points have more weight in the model
>> despite
>> > the lower number.
>> >
>> > Thinning function removes points that are too close to each other (or
>> in a
>> > space where variable data is not available). It is used when there are
>> > points that are too clustered as a result of sampling (but it does not
>> > correspond to the actual distribution). In this function you can
>> determine
>> > the minimum distance between the points.
>> >
>> > thinning() is from package spThin (URL:
>> > https://cran.r-project.org/web/packages/spThin)
>> >
>> >
>> > Finally, are your data really presence/absence data? did you go to at
>> 3355
>> > cells and detect presence/absence of the species? spatial models are
>> > different if we have absences, pseudoabsences or backround. The type of
>> > absence data is important for choosing a model.
>> >
>> >
>> > I'm sorry I couldn't answer your questions
>> >
>> >
>> >
>> > Kind regards,
>> >
>> >
>> > Olga Boet
>> > Documentalista de la col·lecció de cordats. CMCNB
>> > *Myrmex*
>> >
>> >
>> > Missatge de Carlos Bautista <[hidden email]> del dia
>> dj., 9
>> > d’abr. 2020 a les 17:52:
>> >
>> >> Dear list members,
>> >>
>> >> I am using gam (from mgcv package in R) to model presence/absence data
>> in
>> >> 3355 cells of 1x1km (151 presences and 3204 absences). Even though I
>> >> include a smooth with the spatial locations in the model to address the
>> >> spatial dependence in my data, the results from a variogram show
>> spatial
>> >> autocorrelation in the residuals of my gam (range=6000 meters). Since
>> I am
>> >> modelling a binary response, using a gamm with a correlation structure
>> is
>> >> not advisable because it "performs poorly with binary data", neither
>> gamm4
>> >> because (although is supposed to be appropriate for binary data) it has
>> >> "no
>> >> facility for nlme style correlation structures".
>> >>
>> >> The alternative I have found is to fit my model using the function
>> magic
>> >> from the same mgcv package. Because I found no examples of how to use
>> >> magic
>> >> for spatially correlated data I have adapted the ?magic example for
>> >> temporally correlated data. The results of the output change the
>> >> coefficients of the model but do not remove the spatial autocorrelation
>> >> and
>> >> the smooth plots show the same effect.
>> >> You can find find the output from my models and figures of the
>> variograms
>> >> and plots of the smooth effects in the following link
>> >>
>> >>
>> https://stackoverflow.com/questions/61110762/gam-with-binomial-distribution-and-with-spatial-autocorrelation-in-r
>> >>
>> >>
>> >> Could someone tell me if there is something wrong in my script? Does
>> >> anyone
>> >> know another alternative to remove the residuals' spatial
>> autocorrelation
>> >> from a binomial gam?
>> >>
>> >> Thank you very much.
>> >> Kind regards,
>> >> Carlos
>> >> --
>> >> Carlos Bautista
>> >> Institute of Nature Conservation
>> >> Polish Academy of Sciences
>> >> Mickiewicza 33
>> >> 31-120 Krakow, Poland
>> >> www.carpathianbear.pl
>> >> www.iop.krakow.pl
>> >>
>> >>         [[alternative HTML version deleted]]
>> >>
>> >> _______________________________________________
>> >> R-sig-Geo mailing list
>> >> [hidden email]
>> >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>> >>
>> >
>>
>> --
>> Carlos Bautista
>> Institute of Nature Conservation
>> Polish Academy of Sciences
>> Mickiewicza 33
>> 31-120 Krakow, Poland
>> www.carpathianbear.pl
>> www.iop.krakow.pl
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
>
> --
> *Manuel Spínola, Ph.D.*
> Instituto Internacional en Conservación y Manejo de Vida Silvestre
> Universidad Nacional
> Apartado 1350-3000
> Heredia
> COSTA RICA
> [hidden email] <[hidden email]>
> [hidden email]
> Teléfono: (506) 8706 - 4662
> Personal website: Lobito de río
> <https://sites.google.com/site/lobitoderio/>
> Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>
>


--
Carlos Bautista
Institute of Nature Conservation
Polish Academy of Sciences
Mickiewicza 33
31-120 Krakow, Poland
www.carpathianbear.pl
www.iop.krakow.pl

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